library(ggplot2)
library(reshape2)
library(dplyr)
library(tidyr)
library(GGally)
library(grid)
"%&%" = function(a,b) paste(a,b,sep="")
my.dir <- '/Volumes/im-lab/nas40t2/hwheeler/cross-tissue/'
out.dir <- '~/GitHub/GenArch/GenArchPaper/OTD_enrichment/'
Pull h2 estimate CI>0 genes for each tissue & rm .\d+ from ensid
for(tistype in c("Tissue-Specific","Tissue-Wide")){
tsfile <- my.dir %&% 'GTEx_' %&% tistype %&% '_local_h2_se_geneinfo_no_description.txt'
ts <- read.table(tsfile,sep='\t',header=T)
tislist <- scan(my.dir %&% 'GTEx_nine_tissues','c')
tislist <- gsub("-","",tislist)
tislist <- c("CrossTissue",tislist)
h2tislist <- "h2." %&% tislist
setislist <- "se." %&% tislist
geneinfo <- ts[,1:3]
for(i in seq_along(tislist)){
h2tis <- h2tislist[i]
setis <- setislist[i]
data <- ts %>% select(AssociatedGeneName, EnsemblGeneID, matches(h2tis), matches(setis)) %>% mutate(ensid=substr(EnsemblGeneID,1,15))
cidata <- data[data[,3]-data[,4]*2>0,] ##pull genes with non-zero confidence intervals
print(tislist[i] %&% " " %&% tistype %&% ": " %&% dim(cidata)[1] %&% " non-zero h2 CI genes")
write.table(cidata, file=out.dir %&% tislist[i] %&% "_" %&% tistype %&% "_non-zeroCIgenes_info.txt",quote=FALSE,row.names = FALSE)
write.table(cidata[,5], file=out.dir %&% tislist[i] %&% "_" %&% tistype %&% "_non-zeroCIgenes_ensid_list.txt",quote=FALSE,row.names = FALSE, col.names = FALSE)
write.table(cidata[,1], file=out.dir %&% tislist[i] %&% "_" %&% tistype %&% "_non-zeroCIgenes_gene_list.txt",quote=FALSE,row.names = FALSE, col.names = FALSE)
for(thresh in c(0,0.01,0.05,0.1)){
h2data <- data[data[,3]>thresh,] ##pull genes with h2 > thresh
print(tislist[i] %&% " " %&% tistype %&% ": " %&% dim(h2data)[1] %&% " h2 > " %&% thresh %&% " genes")
write.table(h2data, file=out.dir %&% tislist[i] %&% "_" %&% tistype %&% "_h2_" %&% thresh %&% "genes_info.txt",quote=FALSE,row.names = FALSE)
write.table(h2data[,5], file=out.dir %&% tislist[i] %&% "_" %&% tistype %&% "_h2_" %&% thresh %&% "genes_ensid_list.txt",quote=FALSE,row.names = FALSE, col.names = FALSE)
write.table(h2data[,1], file=out.dir %&% tislist[i] %&% "_" %&% tistype %&% "_h2_" %&% thresh %&% "genes_gene_list.txt",quote=FALSE,row.names = FALSE, col.names = FALSE)
}
}
}
## [1] "CrossTissue Tissue-Specific: 2938 non-zero h2 CI genes"
## [1] "CrossTissue Tissue-Specific: 17007 h2 > 0 genes"
## [1] "CrossTissue Tissue-Specific: 10282 h2 > 0.01 genes"
## [1] "CrossTissue Tissue-Specific: 5042 h2 > 0.05 genes"
## [1] "CrossTissue Tissue-Specific: 2715 h2 > 0.1 genes"
## [1] "AdiposeSubcutaneous Tissue-Specific: 207 non-zero h2 CI genes"
## [1] "AdiposeSubcutaneous Tissue-Specific: 17007 h2 > 0 genes"
## [1] "AdiposeSubcutaneous Tissue-Specific: 6908 h2 > 0.01 genes"
## [1] "AdiposeSubcutaneous Tissue-Specific: 2463 h2 > 0.05 genes"
## [1] "AdiposeSubcutaneous Tissue-Specific: 650 h2 > 0.1 genes"
## [1] "ArteryTibial Tissue-Specific: 306 non-zero h2 CI genes"
## [1] "ArteryTibial Tissue-Specific: 17007 h2 > 0 genes"
## [1] "ArteryTibial Tissue-Specific: 7779 h2 > 0.01 genes"
## [1] "ArteryTibial Tissue-Specific: 3126 h2 > 0.05 genes"
## [1] "ArteryTibial Tissue-Specific: 892 h2 > 0.1 genes"
## [1] "HeartLeftVentricle Tissue-Specific: 298 non-zero h2 CI genes"
## [1] "HeartLeftVentricle Tissue-Specific: 17007 h2 > 0 genes"
## [1] "HeartLeftVentricle Tissue-Specific: 7796 h2 > 0.01 genes"
## [1] "HeartLeftVentricle Tissue-Specific: 4012 h2 > 0.05 genes"
## [1] "HeartLeftVentricle Tissue-Specific: 1614 h2 > 0.1 genes"
## [1] "Lung Tissue-Specific: 223 non-zero h2 CI genes"
## [1] "Lung Tissue-Specific: 17007 h2 > 0 genes"
## [1] "Lung Tissue-Specific: 7143 h2 > 0.01 genes"
## [1] "Lung Tissue-Specific: 2738 h2 > 0.05 genes"
## [1] "Lung Tissue-Specific: 797 h2 > 0.1 genes"
## [1] "MuscleSkeletal Tissue-Specific: 434 non-zero h2 CI genes"
## [1] "MuscleSkeletal Tissue-Specific: 17007 h2 > 0 genes"
## [1] "MuscleSkeletal Tissue-Specific: 7371 h2 > 0.01 genes"
## [1] "MuscleSkeletal Tissue-Specific: 2356 h2 > 0.05 genes"
## [1] "MuscleSkeletal Tissue-Specific: 622 h2 > 0.1 genes"
## [1] "NerveTibial Tissue-Specific: 379 non-zero h2 CI genes"
## [1] "NerveTibial Tissue-Specific: 17006 h2 > 0 genes"
## [1] "NerveTibial Tissue-Specific: 7797 h2 > 0.01 genes"
## [1] "NerveTibial Tissue-Specific: 3491 h2 > 0.05 genes"
## [1] "NerveTibial Tissue-Specific: 1216 h2 > 0.1 genes"
## [1] "SkinSunExposed(Lowerleg) Tissue-Specific: 449 non-zero h2 CI genes"
## [1] "SkinSunExposed(Lowerleg) Tissue-Specific: 17007 h2 > 0 genes"
## [1] "SkinSunExposed(Lowerleg) Tissue-Specific: 8604 h2 > 0.01 genes"
## [1] "SkinSunExposed(Lowerleg) Tissue-Specific: 3589 h2 > 0.05 genes"
## [1] "SkinSunExposed(Lowerleg) Tissue-Specific: 1097 h2 > 0.1 genes"
## [1] "Thyroid Tissue-Specific: 442 non-zero h2 CI genes"
## [1] "Thyroid Tissue-Specific: 17007 h2 > 0 genes"
## [1] "Thyroid Tissue-Specific: 7757 h2 > 0.01 genes"
## [1] "Thyroid Tissue-Specific: 3141 h2 > 0.05 genes"
## [1] "Thyroid Tissue-Specific: 1010 h2 > 0.1 genes"
## [1] "WholeBlood Tissue-Specific: 490 non-zero h2 CI genes"
## [1] "WholeBlood Tissue-Specific: 17007 h2 > 0 genes"
## [1] "WholeBlood Tissue-Specific: 8504 h2 > 0.01 genes"
## [1] "WholeBlood Tissue-Specific: 2918 h2 > 0.05 genes"
## [1] "WholeBlood Tissue-Specific: 819 h2 > 0.1 genes"
## [1] "CrossTissue Tissue-Wide: 2938 non-zero h2 CI genes"
## [1] "CrossTissue Tissue-Wide: 17007 h2 > 0 genes"
## [1] "CrossTissue Tissue-Wide: 10282 h2 > 0.01 genes"
## [1] "CrossTissue Tissue-Wide: 5042 h2 > 0.05 genes"
## [1] "CrossTissue Tissue-Wide: 2715 h2 > 0.1 genes"
## [1] "AdiposeSubcutaneous Tissue-Wide: 1127 non-zero h2 CI genes"
## [1] "AdiposeSubcutaneous Tissue-Wide: 17007 h2 > 0 genes"
## [1] "AdiposeSubcutaneous Tissue-Wide: 6236 h2 > 0.01 genes"
## [1] "AdiposeSubcutaneous Tissue-Wide: 3407 h2 > 0.05 genes"
## [1] "AdiposeSubcutaneous Tissue-Wide: 1736 h2 > 0.1 genes"
## [1] "ArteryTibial Tissue-Wide: 1171 non-zero h2 CI genes"
## [1] "ArteryTibial Tissue-Wide: 17007 h2 > 0 genes"
## [1] "ArteryTibial Tissue-Wide: 6314 h2 > 0.01 genes"
## [1] "ArteryTibial Tissue-Wide: 3546 h2 > 0.05 genes"
## [1] "ArteryTibial Tissue-Wide: 1889 h2 > 0.1 genes"
## [1] "HeartLeftVentricle Tissue-Wide: 749 non-zero h2 CI genes"
## [1] "HeartLeftVentricle Tissue-Wide: 17007 h2 > 0 genes"
## [1] "HeartLeftVentricle Tissue-Wide: 6352 h2 > 0.01 genes"
## [1] "HeartLeftVentricle Tissue-Wide: 3865 h2 > 0.05 genes"
## [1] "HeartLeftVentricle Tissue-Wide: 2179 h2 > 0.1 genes"
## [1] "Lung Tissue-Wide: 929 non-zero h2 CI genes"
## [1] "Lung Tissue-Wide: 17007 h2 > 0 genes"
## [1] "Lung Tissue-Wide: 6331 h2 > 0.01 genes"
## [1] "Lung Tissue-Wide: 3387 h2 > 0.05 genes"
## [1] "Lung Tissue-Wide: 1693 h2 > 0.1 genes"
## [1] "MuscleSkeletal Tissue-Wide: 1063 non-zero h2 CI genes"
## [1] "MuscleSkeletal Tissue-Wide: 17007 h2 > 0 genes"
## [1] "MuscleSkeletal Tissue-Wide: 6049 h2 > 0.01 genes"
## [1] "MuscleSkeletal Tissue-Wide: 2814 h2 > 0.05 genes"
## [1] "MuscleSkeletal Tissue-Wide: 1331 h2 > 0.1 genes"
## [1] "NerveTibial Tissue-Wide: 1466 non-zero h2 CI genes"
## [1] "NerveTibial Tissue-Wide: 17006 h2 > 0 genes"
## [1] "NerveTibial Tissue-Wide: 6594 h2 > 0.01 genes"
## [1] "NerveTibial Tissue-Wide: 4057 h2 > 0.05 genes"
## [1] "NerveTibial Tissue-Wide: 2399 h2 > 0.1 genes"
## [1] "SkinSunExposed(Lowerleg) Tissue-Wide: 1198 non-zero h2 CI genes"
## [1] "SkinSunExposed(Lowerleg) Tissue-Wide: 17007 h2 > 0 genes"
## [1] "SkinSunExposed(Lowerleg) Tissue-Wide: 6504 h2 > 0.01 genes"
## [1] "SkinSunExposed(Lowerleg) Tissue-Wide: 3530 h2 > 0.05 genes"
## [1] "SkinSunExposed(Lowerleg) Tissue-Wide: 1809 h2 > 0.1 genes"
## [1] "Thyroid Tissue-Wide: 1327 non-zero h2 CI genes"
## [1] "Thyroid Tissue-Wide: 17006 h2 > 0 genes"
## [1] "Thyroid Tissue-Wide: 6590 h2 > 0.01 genes"
## [1] "Thyroid Tissue-Wide: 3758 h2 > 0.05 genes"
## [1] "Thyroid Tissue-Wide: 2082 h2 > 0.1 genes"
## [1] "WholeBlood Tissue-Wide: 945 non-zero h2 CI genes"
## [1] "WholeBlood Tissue-Wide: 17007 h2 > 0 genes"
## [1] "WholeBlood Tissue-Wide: 5426 h2 > 0.01 genes"
## [1] "WholeBlood Tissue-Wide: 2586 h2 > 0.05 genes"
## [1] "WholeBlood Tissue-Wide: 1261 h2 > 0.1 genes"
write(data$ensid, file=out.dir %&% "full_tested_ensid_list.txt",ncolumns=1)
Define known genes for 7 WTCCC diseases based on the GWAS catalog and make list of ALL genes in catalog
#catalog used:
gwasfile <- my.dir %&% 'gwas_catalog_v1.0-downloaded_2015-06-02.tsv'
# gwas <- data.frame(read.table(gwasfile,header=TRUE,sep='\t',quote="",comment.char="",as.is=TRUE)) #for reference
runPERL <- "perl " %&% my.dir %&% "24_define_disease_genes.pl " %&% gwasfile
system(runPERL)
Disease gene enrichment in top h2 genes by tissue
lci<-function(x) quantile(x, c(.025, 0.975),na.rm=T)[[1]]
uci<-function(x) quantile(x, c(.025, 0.975),na.rm=T)[[2]]
##is testvec signficanty enriched for setvec? make n samples of size(testvec) from fullvec and count overlap
enrichment <- function(setvec, testvec, fullvec, nperms = 1000){
obs <- length(testvec[testvec %in% setvec])
counts <- vector()
for(i in 1:nperms){
rantest <- base::sample(fullvec,length(testvec),replace=FALSE)
cnt <- length(rantest[rantest %in% setvec])
counts <- c(counts,cnt)
}
empp <- mean(counts>obs)
meanc <- mean(counts)
lc <- lci(counts)
uc <- uci(counts)
return(c(obs,meanc,lc,uc,empp))
}
set.seed(42)
fullgenelist <- as.character(ts$AssociatedGeneName)
dislist <- c("BD","CAD","HT","T1D","T2D","CD","RA","ALL")
nperms <- 10000
for(thresh in c("non-zeroCI","h2_0.1","h2_0.05","h2_0.01")){
results <- data.frame(type=character(0),dis=character(0),tis=character(0),obsOverlap=double(0),meanOverlap=double(0),lCI=double(0),uCI=double(0),empPval=double(0))
for(tistype in c("Tissue-Specific","Tissue-Wide")){
for(i in seq_along(dislist)){
dis <- dislist[i]
disgenes <- scan(out.dir %&% "gwas." %&% dis %&% ".tsv","character")
for(j in seq_along(tislist)){
tis <- tislist[j]
tisgenes <- scan(out.dir %&% tis %&% '_' %&% tistype %&% '_' %&% thresh %&% 'genes_gene_list.txt','character')
res <- enrichment(disgenes,tisgenes,fullgenelist,nperms=nperms)
resvec <- data.frame(type=tistype,dis=dis,tis=tis,obsOverlap=res[1],meanOverlap=res[2],lCI=res[3],uCI=res[4],empPval=res[5])
results <- rbind(results,resvec)
}
}
}
sortres <- arrange(results,empPval)
write.table(sortres,file=out.dir %&% "GWAS_catalog_disease_gene5e-8_enrichment_in_" %&% thresh %&% "_genes_by_tissue_" %&% nperms %&% "perms.txt",row.names=FALSE,quote=FALSE)
print("GWAS catalog disease gene enrichment in " %&% thresh %&% " genes by tissue " %&% nperms %&% " perms:")
print(head(sortres,n=20L))
}
## [1] "GWAS catalog disease gene enrichment in non-zeroCI genes by tissue 10000 perms:"
## type dis tis obsOverlap meanOverlap lCI
## 1 Tissue-Specific ALL Lung 82 56.2072 44
## 2 Tissue-Wide ALL WholeBlood 290 238.4374 213
## 3 Tissue-Specific ALL Thyroid 140 111.5018 94
## 4 Tissue-Specific CD MuscleSkeletal 17 8.2607 3
## 5 Tissue-Specific T2D Lung 5 1.1964 0
## 6 Tissue-Specific HT Lung 2 0.2605 0
## 7 Tissue-Specific T1D WholeBlood 6 2.1424 0
## 8 Tissue-Specific CAD HeartLeftVentricle 4 1.3001 0
## 9 Tissue-Specific CAD MuscleSkeletal 5 1.9170 0
## 10 Tissue-Specific ALL MuscleSkeletal 128 109.6354 92
## 11 Tissue-Specific ALL HeartLeftVentricle 90 75.1409 61
## 12 Tissue-Wide ALL Lung 260 234.2492 209
## 13 Tissue-Specific T2D SkinSunExposed(Lowerleg) 5 2.4301 0
## 14 Tissue-Specific T1D HeartLeftVentricle 3 1.2897 0
## 15 Tissue-Specific RA HeartLeftVentricle 5 2.4808 0
## 16 Tissue-Specific ALL WholeBlood 139 123.5206 105
## 17 Tissue-Specific HT HeartLeftVentricle 1 0.3530 0
## 18 Tissue-Specific RA WholeBlood 7 4.0554 1
## 19 Tissue-Specific T2D WholeBlood 5 2.6384 0
## 20 Tissue-Specific ALL ArteryTibial 88 77.2368 63
## uCI empPval
## 1 69.000 0.0000
## 2 264.000 0.0003
## 3 129.025 0.0009
## 4 14.000 0.0013
## 5 4.000 0.0015
## 6 2.000 0.0020
## 7 5.000 0.0042
## 8 4.000 0.0098
## 9 5.000 0.0111
## 10 128.000 0.0206
## 11 90.000 0.0215
## 12 260.000 0.0223
## 13 6.000 0.0351
## 14 4.000 0.0390
## 15 6.000 0.0413
## 16 142.000 0.0444
## 17 2.000 0.0484
## 18 8.000 0.0499
## 19 6.000 0.0508
## 20 92.000 0.0687
## [1] "GWAS catalog disease gene enrichment in h2_0.1 genes by tissue 10000 perms:"
## type dis tis obsOverlap meanOverlap
## 1 Tissue-Wide ALL WholeBlood 366 318.1679
## 2 Tissue-Specific T1D WholeBlood 8 3.5559
## 3 Tissue-Specific CD MuscleSkeletal 20 11.8198
## 4 Tissue-Specific HT Lung 3 0.9179
## 5 Tissue-Specific CAD MuscleSkeletal 6 2.7539
## 6 Tissue-Wide ALL ArteryTibial 512 476.7326
## 7 Tissue-Specific ALL Thyroid 278 254.8368
## 8 Tissue-Specific BD NerveTibial 3 1.3102
## 9 Tissue-Specific CD HeartLeftVentricle 39 30.5712
## 10 Tissue-Specific HT NerveTibial 3 1.4320
## 11 Tissue-Specific ALL MuscleSkeletal 174 156.8398
## 12 Tissue-Wide HT NerveTibial 5 2.8251
## 13 Tissue-Wide CD WholeBlood 31 23.9342
## 14 Tissue-Specific ALL WholeBlood 225 206.5587
## 15 Tissue-Wide BD Thyroid 4 2.2264
## 16 Tissue-Specific ALL HeartLeftVentricle 430 407.0677
## 17 Tissue-Specific ALL Lung 217 201.2189
## 18 Tissue-Wide ALL Lung 449 426.8637
## 19 Tissue-Specific T2D Thyroid 8 5.4615
## 20 Tissue-Specific BD SkinSunExposed(Lowerleg) 2 1.1466
## lCI uCI empPval
## 1 289.000 347 0.0005
## 2 1.000 7 0.0080
## 3 6.000 19 0.0086
## 4 0.000 3 0.0112
## 5 0.000 6 0.0182
## 6 442.000 512 0.0245
## 7 229.000 282 0.0405
## 8 0.000 4 0.0413
## 9 21.000 41 0.0474
## 10 0.000 4 0.0482
## 11 136.000 178 0.0505
## 12 0.000 6 0.0520
## 13 15.000 33 0.0536
## 14 183.000 230 0.0585
## 15 0.000 5 0.0615
## 16 374.000 440 0.0799
## 17 178.000 225 0.0857
## 18 394.000 460 0.0907
## 19 1.975 10 0.0916
## 20 0.000 3 0.0996
## [1] "GWAS catalog disease gene enrichment in h2_0.05 genes by tissue 10000 perms:"
## type dis tis obsOverlap meanOverlap lCI
## 1 Tissue-Specific HT Lung 7 3.2244 0
## 2 Tissue-Wide BD AdiposeSubcutaneous 7 3.6278 1
## 3 Tissue-Wide ALL MuscleSkeletal 754 709.8752 669
## 4 Tissue-Specific BD NerveTibial 7 3.7060 1
## 5 Tissue-Wide ALL WholeBlood 692 652.2293 613
## 6 Tissue-Wide ALL NerveTibial 1070 1023.2204 975
## 7 Tissue-Wide ALL ArteryTibial 938 894.7990 850
## 8 Tissue-Specific T2D Lung 20 14.8128 8
## 9 Tissue-Wide ALL Thyroid 984 947.6142 904
## 10 Tissue-Specific ALL Thyroid 826 792.2935 749
## 11 Tissue-Specific HT AdiposeSubcutaneous 5 2.8948 0
## 12 Tissue-Specific BD SkinSunExposed(Lowerleg) 6 3.7760 1
## 13 Tissue-Specific CAD AdiposeSubcutaneous 15 10.8874 5
## 14 Tissue-Specific BD WholeBlood 5 3.0924 0
## 15 Tissue-Specific CD ArteryTibial 69 59.3060 46
## 16 Tissue-Wide BD Thyroid 6 3.9805 1
## 17 Tissue-Specific CAD WholeBlood 17 12.8854 7
## 18 Tissue-Specific ALL MuscleSkeletal 621 594.5320 556
## 19 Tissue-Wide T2D ArteryTibial 24 19.1016 12
## 20 Tissue-Specific BD Thyroid 5 3.3069 0
## uCI empPval
## 1 7.000 0.0089
## 2 7.000 0.0178
## 3 751.000 0.0183
## 4 7.000 0.0200
## 5 692.000 0.0240
## 6 1071.025 0.0274
## 7 940.000 0.0284
## 8 22.000 0.0559
## 9 993.000 0.0567
## 10 836.000 0.0581
## 11 6.000 0.0613
## 12 7.000 0.0632
## 13 17.000 0.0711
## 14 6.000 0.0716
## 15 73.000 0.0738
## 16 8.000 0.0800
## 17 20.000 0.0820
## 18 633.000 0.0852
## 19 27.000 0.0865
## 20 7.000 0.0976
## [1] "GWAS catalog disease gene enrichment in h2_0.01 genes by tissue 10000 perms:"
## type dis tis obsOverlap meanOverlap
## 1 Tissue-Wide ALL WholeBlood 1443 1368.5416
## 2 Tissue-Wide BD NerveTibial 12 6.9685
## 3 Tissue-Wide ALL ArteryTibial 1658 1592.5260
## 4 Tissue-Wide BD Thyroid 11 6.9748
## 5 Tissue-Specific ALL NerveTibial 2028 1966.7564
## 6 Tissue-Wide ALL MuscleSkeletal 1582 1525.7863
## 7 Tissue-Specific BD SkinSunExposed(Lowerleg) 13 9.1227
## 8 Tissue-Specific T2D Lung 48 38.7017
## 9 Tissue-Specific BD ArteryTibial 12 8.2039
## 10 Tissue-Wide ALL NerveTibial 1715 1662.6956
## 11 Tissue-Specific HT NerveTibial 13 9.1965
## 12 Tissue-Wide ALL Thyroid 1715 1662.3288
## 13 Tissue-Wide BD WholeBlood 9 5.7428
## 14 Tissue-Wide CAD MuscleSkeletal 34 26.6712
## 15 Tissue-Wide BD AdiposeSubcutaneous 10 6.6147
## 16 Tissue-Specific CD AdiposeSubcutaneous 145 131.2328
## 17 Tissue-Specific ALL Thyroid 2000 1956.6711
## 18 Tissue-Wide T2D AdiposeSubcutaneous 40 33.7749
## 19 Tissue-Specific ALL AdiposeSubcutaneous 1780 1741.8881
## 20 Tissue-Wide BD ArteryTibial 9 6.7149
## lCI uCI empPval
## 1 1317 1421 0.0032
## 2 3 11 0.0033
## 3 1540 1646 0.0077
## 4 3 11 0.0139
## 5 1912 2022 0.0142
## 6 1473 1579 0.0178
## 7 5 13 0.0179
## 8 30 48 0.0189
## 9 4 12 0.0194
## 10 1609 1716 0.0252
## 11 5 14 0.0256
## 12 1608 1716 0.0265
## 13 2 10 0.0303
## 14 19 35 0.0306
## 15 3 11 0.0322
## 16 114 149 0.0524
## 17 1902 2013 0.0620
## 18 25 43 0.0680
## 19 1687 1796 0.0818
## 20 3 11 0.0869
distribution of h2 for disease vs non disease
dislist <- c("BD","CAD","HT","T1D","T2D","CD","RA","ALL")
tislist <- c("CrossTissue","AdiposeSubcutaneous","ArteryTibial","HeartLeftVentricle","Lung","MuscleSkeletal","NerveTibial","SkinSunExposed(Lowerleg)","Thyroid","WholeBlood")
typelist<-c("Tissue-Specific","Tissue-Wide")
for(thresh in c(0.1,0.05,0)){
for(tistype in typelist){
for(tis in tislist){
info <- read.table(out.dir %&% tis %&% '_' %&% tistype %&% '_h2_' %&% thresh %&% 'genes_info.txt',header=T)
finaldf <- data.frame(AssociatedGeneName=character(0),EnsemblGeneID=character(0),h2=double(0),se=double(0),ensid=character(0),diseaseGene=logical(0L),disease=character(0))
for(dis in dislist){
setvec <- scan(out.dir %&% "gwas." %&% dis %&% ".tsv","character")
disinfo <- info %>% mutate(diseaseGene=(info[,1] %in% setvec),disease=dis)
colnames(disinfo) <- c("AssociatedGeneName","EnsemblGeneID","h2","se","ensid","diseaseGene","disease")
finaldf <- rbind(finaldf,disinfo)
}
p<-ggplot(finaldf,aes(x=finaldf[,3],fill=diseaseGene,color=diseaseGene)) + facet_wrap(~disease,ncol=2) + geom_density(alpha=0.3) + xlab("h2") + ggtitle(tistype %&% ' ' %&% tis %&% ' h2 > ' %&% thresh)
print(p)
p<-ggplot(finaldf,aes(y=finaldf[,3],x=diseaseGene)) + facet_wrap(~disease,ncol=4) + geom_jitter(aes(colour=diseaseGene),alpha=0.3,position = position_jitter(width = .15)) + geom_boxplot(alpha=0,outlier.size=NA) + xlab("diseaseGene") + ylab("h2") + ggtitle(tistype %&% ' ' %&% tis %&% ' h2 > ' %&% thresh) +theme_bw() + theme(legend.position="none")
print(p)
}
}
}
